Semiparametric regression calibration for general hazard models in survival analysis with covariate measurement error; surprising performance under linear hazard.

ABSTRACT: Observational epidemiological studies often confront the problem of estimating exposure-disease relationships when the exposure is not measured exactly. Regression calibration (RC) is a common approach to correct for bias in regression analysis with covariate measurement error. In survival analysis with covariate measurement error, it is well known that the RC estimator may be biased when the hazard is an exponential function of the covariates. In the paper, we investigate the RC estimator with general hazard functions, including exponential and linear functions of the covariates. When the hazard is a linear function of the covariates, we show that a risk set regression calibration (RRC) is consistent and robust to a working model for the calibration function. Under exponential hazard mode

SUBMITTER: Wang CY 

PROVIDER: S-EPMC7746575 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature